1. Natural Language Processing
Part-of-Speech Tagging

2. Parts of Speech 8–10 traditional parts of speech Variously called:
Noun, verb, adjective, adverb, preposition, article, interjection, pronoun, conjunction, …
Variously called:
Parts of speech, lexical categories, word classes, morphological classes, lexical tags, ...
Lots of debate within linguistics about the number, nature, and universality of these
We’ll completely ignore this debate

3. POS Examples N noun chair, bandwidth, pacing
V verb study, debate, munch
ADJ adjective purple, tall, ridiculous
ADV adverb unfortunately, slowly
P preposition of, by, to
DET determiner the, a, that, those
INT interjection ouch, hey
PRO pronoun I, me, mine
CONJ conjunction and, but, for, because

4. POS Tagging
The process of assigning a part-of-speech or lexical class marker to each word in a collection
WORD tag
the DET
koala N
put V
the DET
keys N
on P
table N

5. Why is POS Tagging Useful?
First step of a vast number of practical tasks
Speech synthesis
How to pronounce “lead”?
INsult inSULT
OBject obJECT
OVERflow overFLOW
DIScount disCOUNT
CONtent conTENT
Parsing
Need to know if a word is an N or V before you can parse
Information extraction
Finding names, relations, etc.
Machine Translation

6. Open and Closed Classes
Closed class: a small fixed membership
Prepositions: of, in, by, …
Auxiliaries: may, can, will had, been, …
Pronouns: I, you, she, mine, his, them, …
Usually function words (short common words which play a role in grammar)
Open class: new ones can be created all the time
English has 4: Nouns, Verbs, Adjectives, Adverbs
Many languages have these 4, but not all!

7. POS Tagging Choosing a Tagset
There are so many parts of speech, potential distinctions we can draw
To do POS tagging, we need to choose a standard set of tags to work with
Could pick very coarse tagsets
N, V, Adj, Adv, …
More commonly used set is finer grained, the “Penn TreeBank tagset”, 45 tags
PRP$, WRB, WP$, VBG
Even more fine-grained tagsets exist

8. Penn TreeBank POS Tagset

9. POS Tagging Words often have more than one POS:
back
The back door = JJ
On my back = NN
Win the voters back = RB
Promised to back the bill = VB
The POS tagging problem is to determine the tag for a particular instance of a word
These examples from Dekang Lin

10. How Hard is POS Tagging? Measuring Ambiguity

11. Evaluation
So once you have you POS tagger running how do you evaluate it?
Overall error rate with respect to a manually annotated gold-standard test set
Error rates on particular tags
Tag confusions ...
Accuracy typically reaches 96–97% for English newswire text
What about Turkish?
What about twitter?

12. Error Analysis Look at a confusion matrix
See what errors are causing problems
Noun (NN) vs ProperNoun (NNP) vs Adj (JJ)
Preterite (VBD) vs Participle (VBN) vs Adjective (JJ)